Model_Building.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "527c82ba-ad90-4b7e-af94-511ce42e2c54",
      "metadata": {
        "id": "527c82ba-ad90-4b7e-af94-511ce42e2c54"
      },
      "outputs": [],
      "source": [
        "new_df = pd.read_csv(\"combined_first_rows.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "8c3903e1-ac49-4198-b276-8def959bcd48",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "8c3903e1-ac49-4198-b276-8def959bcd48",
        "outputId": "681131ec-6137-4cb7-c701-d3929e81835c"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "new_df"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-b7caed91-9dea-4c25-beec-9fdf43a7b418\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time [s]</th>\n",
              "      <th>Pressure [cmH2O]</th>\n",
              "      <th>Flow [L/s]</th>\n",
              "      <th>V_tidal [L]</th>\n",
              "      <th>Chest [mm]</th>\n",
              "      <th>Abd [mm]</th>\n",
              "      <th>Inspiratory Indicies</th>\n",
              "      <th>Time (Aeration Data)_[s]</th>\n",
              "      <th>Global Aeration</th>\n",
              "      <th>Inspiratory Indicies (Aeration Data)</th>\n",
              "      <th>...</th>\n",
              "      <th>Smoking Frequency</th>\n",
              "      <th>Time as a smoker</th>\n",
              "      <th>History of Vaping (Y/N)</th>\n",
              "      <th>Current Vaper (Y/N)</th>\n",
              "      <th>How long since you quit vaping</th>\n",
              "      <th>Vaping Frequency</th>\n",
              "      <th>Time as a vaper</th>\n",
              "      <th>Trial Classification</th>\n",
              "      <th>Chest Depth [mm]</th>\n",
              "      <th>Chest Width [mm]</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.01</td>\n",
              "      <td>-3.400773</td>\n",
              "      <td>0.713827</td>\n",
              "      <td>0.299421</td>\n",
              "      <td>830.612330</td>\n",
              "      <td>735.557370</td>\n",
              "      <td>407</td>\n",
              "      <td>0.02</td>\n",
              "      <td>3.876638e+03</td>\n",
              "      <td>203.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Normal Female</td>\n",
              "      <td>20.0</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.01</td>\n",
              "      <td>2.703454</td>\n",
              "      <td>-0.610317</td>\n",
              "      <td>0.117958</td>\n",
              "      <td>1048.261347</td>\n",
              "      <td>1031.678708</td>\n",
              "      <td>209</td>\n",
              "      <td>0.02</td>\n",
              "      <td>6.814612e+03</td>\n",
              "      <td>104.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4 times daily</td>\n",
              "      <td>2 years</td>\n",
              "      <td>Asthmatic Female</td>\n",
              "      <td>25.0</td>\n",
              "      <td>33.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.01</td>\n",
              "      <td>-1.619927</td>\n",
              "      <td>0.459778</td>\n",
              "      <td>0.660224</td>\n",
              "      <td>825.950946</td>\n",
              "      <td>713.628807</td>\n",
              "      <td>196</td>\n",
              "      <td>0.02</td>\n",
              "      <td>6.902727e+03</td>\n",
              "      <td>98.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Normal Female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.01</td>\n",
              "      <td>1.823758</td>\n",
              "      <td>-0.510568</td>\n",
              "      <td>-0.248067</td>\n",
              "      <td>1034.860261</td>\n",
              "      <td>908.856548</td>\n",
              "      <td>63</td>\n",
              "      <td>0.02</td>\n",
              "      <td>5.109781e+03</td>\n",
              "      <td>31.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Normal Male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>38.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.386208</td>\n",
              "      <td>0.193579</td>\n",
              "      <td>0.003872</td>\n",
              "      <td>889.221901</td>\n",
              "      <td>754.629966</td>\n",
              "      <td>304</td>\n",
              "      <td>0.02</td>\n",
              "      <td>5.888652e+03</td>\n",
              "      <td>152.0</td>\n",
              "      <td>...</td>\n",
              "      <td>3-4 times yearly</td>\n",
              "      <td>10 years</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Normal Male</td>\n",
              "      <td>23.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>-0.01</td>\n",
              "      <td>-1.287359</td>\n",
              "      <td>0.386368</td>\n",
              "      <td>0.671871</td>\n",
              "      <td>788.268708</td>\n",
              "      <td>691.503282</td>\n",
              "      <td>524</td>\n",
              "      <td>0.02</td>\n",
              "      <td>6.669932e+03</td>\n",
              "      <td>262.0</td>\n",
              "      <td>...</td>\n",
              "      <td>3-4 times daily</td>\n",
              "      <td>3 years</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Smoker Female</td>\n",
              "      <td>21.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>-0.01</td>\n",
              "      <td>-1.823758</td>\n",
              "      <td>0.510967</td>\n",
              "      <td>0.297882</td>\n",
              "      <td>740.842547</td>\n",
              "      <td>601.545280</td>\n",
              "      <td>248</td>\n",
              "      <td>0.02</td>\n",
              "      <td>2.166055e+06</td>\n",
              "      <td>124.0</td>\n",
              "      <td>...</td>\n",
              "      <td>less than once daily</td>\n",
              "      <td>7 years</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Smoker Female</td>\n",
              "      <td>20.0</td>\n",
              "      <td>25.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>0.00</td>\n",
              "      <td>4.087364</td>\n",
              "      <td>-0.416796</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>974.658648</td>\n",
              "      <td>912.772954</td>\n",
              "      <td>191</td>\n",
              "      <td>0.00</td>\n",
              "      <td>8.472019e+03</td>\n",
              "      <td>124.0</td>\n",
              "      <td>...</td>\n",
              "      <td>10 daily</td>\n",
              "      <td>3 years</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>once hourly</td>\n",
              "      <td>4 years</td>\n",
              "      <td>Vaper Male</td>\n",
              "      <td>25.0</td>\n",
              "      <td>34.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>0.00</td>\n",
              "      <td>-1.898854</td>\n",
              "      <td>0.522983</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>958.539885</td>\n",
              "      <td>843.194807</td>\n",
              "      <td>451</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.020595e+04</td>\n",
              "      <td>95.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10 times daily</td>\n",
              "      <td>3 years</td>\n",
              "      <td>Vaper Male</td>\n",
              "      <td>25.5</td>\n",
              "      <td>36.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>-0.01</td>\n",
              "      <td>-1.083527</td>\n",
              "      <td>0.386368</td>\n",
              "      <td>0.128367</td>\n",
              "      <td>797.721864</td>\n",
              "      <td>701.581229</td>\n",
              "      <td>237</td>\n",
              "      <td>0.02</td>\n",
              "      <td>9.650998e+02</td>\n",
              "      <td>118.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Asthmatic Female</td>\n",
              "      <td>23.0</td>\n",
              "      <td>27.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>80 rows × 31 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b7caed91-9dea-4c25-beec-9fdf43a7b418')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b7caed91-9dea-4c25-beec-9fdf43a7b418 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b7caed91-9dea-4c25-beec-9fdf43a7b418');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f22f6f12-376b-4eaa-a37b-96f0832afd3f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f22f6f12-376b-4eaa-a37b-96f0832afd3f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f22f6f12-376b-4eaa-a37b-96f0832afd3f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_9d55da27-69f2-46f1-b721-3450e1cc200e\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('new_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_9d55da27-69f2-46f1-b721-3450e1cc200e button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('new_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "    Time [s]  Pressure [cmH2O]  Flow [L/s]  V_tidal [L]   Chest [mm]  \\\n",
              "0      -0.01         -3.400773    0.713827     0.299421   830.612330   \n",
              "1      -0.01          2.703454   -0.610317     0.117958  1048.261347   \n",
              "2      -0.01         -1.619927    0.459778     0.660224   825.950946   \n",
              "3      -0.01          1.823758   -0.510568    -0.248067  1034.860261   \n",
              "4      -0.01         -0.386208    0.193579     0.003872   889.221901   \n",
              "..       ...               ...         ...          ...          ...   \n",
              "75     -0.01         -1.287359    0.386368     0.671871   788.268708   \n",
              "76     -0.01         -1.823758    0.510967     0.297882   740.842547   \n",
              "77      0.00          4.087364   -0.416796     0.000000   974.658648   \n",
              "78      0.00         -1.898854    0.522983     0.000000   958.539885   \n",
              "79     -0.01         -1.083527    0.386368     0.128367   797.721864   \n",
              "\n",
              "       Abd [mm]  Inspiratory Indicies  Time (Aeration Data)_[s]  \\\n",
              "0    735.557370                   407                      0.02   \n",
              "1   1031.678708                   209                      0.02   \n",
              "2    713.628807                   196                      0.02   \n",
              "3    908.856548                    63                      0.02   \n",
              "4    754.629966                   304                      0.02   \n",
              "..          ...                   ...                       ...   \n",
              "75   691.503282                   524                      0.02   \n",
              "76   601.545280                   248                      0.02   \n",
              "77   912.772954                   191                      0.00   \n",
              "78   843.194807                   451                      0.00   \n",
              "79   701.581229                   237                      0.02   \n",
              "\n",
              "    Global Aeration  Inspiratory Indicies (Aeration Data)  ...  \\\n",
              "0      3.876638e+03                                 203.0  ...   \n",
              "1      6.814612e+03                                 104.0  ...   \n",
              "2      6.902727e+03                                  98.0  ...   \n",
              "3      5.109781e+03                                  31.0  ...   \n",
              "4      5.888652e+03                                 152.0  ...   \n",
              "..              ...                                   ...  ...   \n",
              "75     6.669932e+03                                 262.0  ...   \n",
              "76     2.166055e+06                                 124.0  ...   \n",
              "77     8.472019e+03                                 124.0  ...   \n",
              "78     1.020595e+04                                  95.0  ...   \n",
              "79     9.650998e+02                                 118.0  ...   \n",
              "\n",
              "       Smoking Frequency Time as a smoker   History of Vaping (Y/N)  \\\n",
              "0                    NaN               NaN                        N   \n",
              "1                    NaN               NaN                        Y   \n",
              "2                    NaN               NaN                        N   \n",
              "3                    NaN               NaN                        N   \n",
              "4       3-4 times yearly          10 years                        N   \n",
              "..                   ...               ...                      ...   \n",
              "75       3-4 times daily          3 years                         N   \n",
              "76  less than once daily           7 years                        N   \n",
              "77              10 daily           3 years                        Y   \n",
              "78                   NaN               NaN                        Y   \n",
              "79                   NaN               NaN                        N   \n",
              "\n",
              "    Current Vaper (Y/N)  How long since you quit vaping Vaping Frequency  \\\n",
              "0                   NaN                             NaN              NaN   \n",
              "1                     Y                             NaN    4 times daily   \n",
              "2                   NaN                             NaN              NaN   \n",
              "3                   NaN                             NaN              NaN   \n",
              "4                   NaN                             NaN              NaN   \n",
              "..                  ...                             ...              ...   \n",
              "75                  NaN                             NaN              NaN   \n",
              "76                  NaN                             NaN              NaN   \n",
              "77                    Y                             NaN     once hourly    \n",
              "78                    Y                             NaN   10 times daily   \n",
              "79                  NaN                             NaN              NaN   \n",
              "\n",
              "   Time as a vaper Trial Classification  Chest Depth [mm] Chest Width [mm]  \n",
              "0              NaN         Normal Female             20.0             28.0  \n",
              "1          2 years      Asthmatic Female             25.0             33.0  \n",
              "2              NaN         Normal Female             22.0             30.0  \n",
              "3              NaN           Normal Male             27.0             38.0  \n",
              "4              NaN           Normal Male             23.0             30.0  \n",
              "..             ...                   ...              ...              ...  \n",
              "75             NaN         Smoker Female             21.0             30.0  \n",
              "76             NaN         Smoker Female             20.0             25.0  \n",
              "77         4 years            Vaper Male             25.0             34.0  \n",
              "78        3 years             Vaper Male             25.5             36.0  \n",
              "79             NaN      Asthmatic Female             23.0             27.0  \n",
              "\n",
              "[80 rows x 31 columns]"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "new_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "07cd5774-0661-4ae9-be7a-2c7ef662458c",
      "metadata": {
        "id": "07cd5774-0661-4ae9-be7a-2c7ef662458c"
      },
      "outputs": [],
      "source": [
        "df1 = new_df.drop('Time [s]', axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "hnxBSgLTkBW_",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hnxBSgLTkBW_",
        "outputId": "2ef40f22-3450-4284-c208-b52ef25eeda1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index(['Pressure [cmH2O]', 'Flow [L/s]', 'V_tidal [L]', 'Chest [mm]',\n",
            "       'Abd [mm]', 'Inspiratory Indicies', 'Time (Aeration Data)_[s]',\n",
            "       'Global Aeration', 'Inspiratory Indicies (Aeration Data)',\n",
            "       'Subject Number', 'Sex (M/F)', 'Height [cm]', 'Weight [kg]',\n",
            "       'Age [years]', 'Asthma (Y/N)', 'Medication', 'Dosage Frequency',\n",
            "       'History of Smoking (Y/N)', 'Current Smoker (Y/N)',\n",
            "       'How long since you quit smoking', 'Smoking Frequency',\n",
            "       'Time as a smoker ', 'History of Vaping (Y/N)', 'Current Vaper (Y/N)',\n",
            "       'How long since you quit vaping', 'Vaping Frequency', 'Time as a vaper',\n",
            "       'Trial Classification ', 'Chest Depth [mm]', 'Chest Width [mm]'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "print(df1.columns)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "5364f22a-4d7d-42eb-8a12-6ef2da24e186",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "5364f22a-4d7d-42eb-8a12-6ef2da24e186",
        "outputId": "f7cb33a5-d68e-414d-f6d6-8cfac3d96c68"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df1"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-554a69c6-1301-4755-bf54-b91ebb9ea480\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pressure [cmH2O]</th>\n",
              "      <th>Flow [L/s]</th>\n",
              "      <th>V_tidal [L]</th>\n",
              "      <th>Chest [mm]</th>\n",
              "      <th>Abd [mm]</th>\n",
              "      <th>Inspiratory Indicies</th>\n",
              "      <th>Time (Aeration Data)_[s]</th>\n",
              "      <th>Global Aeration</th>\n",
              "      <th>Inspiratory Indicies (Aeration Data)</th>\n",
              "      <th>Subject Number</th>\n",
              "      <th>...</th>\n",
              "      <th>Smoking Frequency</th>\n",
              "      <th>Time as a smoker</th>\n",
              "      <th>History of Vaping (Y/N)</th>\n",
              "      <th>Current Vaper (Y/N)</th>\n",
              "      <th>How long since you quit vaping</th>\n",
              "      <th>Vaping Frequency</th>\n",
              "      <th>Time as a vaper</th>\n",
              "      <th>Trial Classification</th>\n",
              "      <th>Chest Depth [mm]</th>\n",
              "      <th>Chest Width [mm]</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-3.400773</td>\n",
              "      <td>0.713827</td>\n",
              "      <td>0.299421</td>\n",
              "      <td>830.612330</td>\n",
              "      <td>735.557370</td>\n",
              "      <td>407</td>\n",
              "      <td>0.02</td>\n",
              "      <td>3.876638e+03</td>\n",
              "      <td>203.0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Normal Female</td>\n",
              "      <td>20.0</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.703454</td>\n",
              "      <td>-0.610317</td>\n",
              "      <td>0.117958</td>\n",
              "      <td>1048.261347</td>\n",
              "      <td>1031.678708</td>\n",
              "      <td>209</td>\n",
              "      <td>0.02</td>\n",
              "      <td>6.814612e+03</td>\n",
              "      <td>104.0</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4 times daily</td>\n",
              "      <td>2 years</td>\n",
              "      <td>Asthmatic Female</td>\n",
              "      <td>25.0</td>\n",
              "      <td>33.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.619927</td>\n",
              "      <td>0.459778</td>\n",
              "      <td>0.660224</td>\n",
              "      <td>825.950946</td>\n",
              "      <td>713.628807</td>\n",
              "      <td>196</td>\n",
              "      <td>0.02</td>\n",
              "      <td>6.902727e+03</td>\n",
              "      <td>98.0</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Normal Female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.823758</td>\n",
              "      <td>-0.510568</td>\n",
              "      <td>-0.248067</td>\n",
              "      <td>1034.860261</td>\n",
              "      <td>908.856548</td>\n",
              "      <td>63</td>\n",
              "      <td>0.02</td>\n",
              "      <td>5.109781e+03</td>\n",
              "      <td>31.0</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Normal Male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>38.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.386208</td>\n",
              "      <td>0.193579</td>\n",
              "      <td>0.003872</td>\n",
              "      <td>889.221901</td>\n",
              "      <td>754.629966</td>\n",
              "      <td>304</td>\n",
              "      <td>0.02</td>\n",
              "      <td>5.888652e+03</td>\n",
              "      <td>152.0</td>\n",
              "      <td>5</td>\n",
              "      <td>...</td>\n",
              "      <td>3-4 times yearly</td>\n",
              "      <td>10 years</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Normal Male</td>\n",
              "      <td>23.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>-1.287359</td>\n",
              "      <td>0.386368</td>\n",
              "      <td>0.671871</td>\n",
              "      <td>788.268708</td>\n",
              "      <td>691.503282</td>\n",
              "      <td>524</td>\n",
              "      <td>0.02</td>\n",
              "      <td>6.669932e+03</td>\n",
              "      <td>262.0</td>\n",
              "      <td>76</td>\n",
              "      <td>...</td>\n",
              "      <td>3-4 times daily</td>\n",
              "      <td>3 years</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Smoker Female</td>\n",
              "      <td>21.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>-1.823758</td>\n",
              "      <td>0.510967</td>\n",
              "      <td>0.297882</td>\n",
              "      <td>740.842547</td>\n",
              "      <td>601.545280</td>\n",
              "      <td>248</td>\n",
              "      <td>0.02</td>\n",
              "      <td>2.166055e+06</td>\n",
              "      <td>124.0</td>\n",
              "      <td>77</td>\n",
              "      <td>...</td>\n",
              "      <td>less than once daily</td>\n",
              "      <td>7 years</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Smoker Female</td>\n",
              "      <td>20.0</td>\n",
              "      <td>25.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>4.087364</td>\n",
              "      <td>-0.416796</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>974.658648</td>\n",
              "      <td>912.772954</td>\n",
              "      <td>191</td>\n",
              "      <td>0.00</td>\n",
              "      <td>8.472019e+03</td>\n",
              "      <td>124.0</td>\n",
              "      <td>78</td>\n",
              "      <td>...</td>\n",
              "      <td>10 daily</td>\n",
              "      <td>3 years</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>once hourly</td>\n",
              "      <td>4 years</td>\n",
              "      <td>Vaper Male</td>\n",
              "      <td>25.0</td>\n",
              "      <td>34.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>-1.898854</td>\n",
              "      <td>0.522983</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>958.539885</td>\n",
              "      <td>843.194807</td>\n",
              "      <td>451</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.020595e+04</td>\n",
              "      <td>95.0</td>\n",
              "      <td>79</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10 times daily</td>\n",
              "      <td>3 years</td>\n",
              "      <td>Vaper Male</td>\n",
              "      <td>25.5</td>\n",
              "      <td>36.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>-1.083527</td>\n",
              "      <td>0.386368</td>\n",
              "      <td>0.128367</td>\n",
              "      <td>797.721864</td>\n",
              "      <td>701.581229</td>\n",
              "      <td>237</td>\n",
              "      <td>0.02</td>\n",
              "      <td>9.650998e+02</td>\n",
              "      <td>118.0</td>\n",
              "      <td>80</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Asthmatic Female</td>\n",
              "      <td>23.0</td>\n",
              "      <td>27.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>80 rows × 30 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-554a69c6-1301-4755-bf54-b91ebb9ea480')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-554a69c6-1301-4755-bf54-b91ebb9ea480 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-554a69c6-1301-4755-bf54-b91ebb9ea480');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-73ab453e-1826-4c32-9eed-50329dc67582\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-73ab453e-1826-4c32-9eed-50329dc67582')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-73ab453e-1826-4c32-9eed-50329dc67582 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_32b1d576-005d-4443-8b59-d4168083659a\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df1')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_32b1d576-005d-4443-8b59-d4168083659a button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df1');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "    Pressure [cmH2O]  Flow [L/s]  V_tidal [L]   Chest [mm]     Abd [mm]  \\\n",
              "0          -3.400773    0.713827     0.299421   830.612330   735.557370   \n",
              "1           2.703454   -0.610317     0.117958  1048.261347  1031.678708   \n",
              "2          -1.619927    0.459778     0.660224   825.950946   713.628807   \n",
              "3           1.823758   -0.510568    -0.248067  1034.860261   908.856548   \n",
              "4          -0.386208    0.193579     0.003872   889.221901   754.629966   \n",
              "..               ...         ...          ...          ...          ...   \n",
              "75         -1.287359    0.386368     0.671871   788.268708   691.503282   \n",
              "76         -1.823758    0.510967     0.297882   740.842547   601.545280   \n",
              "77          4.087364   -0.416796     0.000000   974.658648   912.772954   \n",
              "78         -1.898854    0.522983     0.000000   958.539885   843.194807   \n",
              "79         -1.083527    0.386368     0.128367   797.721864   701.581229   \n",
              "\n",
              "    Inspiratory Indicies  Time (Aeration Data)_[s]  Global Aeration  \\\n",
              "0                    407                      0.02     3.876638e+03   \n",
              "1                    209                      0.02     6.814612e+03   \n",
              "2                    196                      0.02     6.902727e+03   \n",
              "3                     63                      0.02     5.109781e+03   \n",
              "4                    304                      0.02     5.888652e+03   \n",
              "..                   ...                       ...              ...   \n",
              "75                   524                      0.02     6.669932e+03   \n",
              "76                   248                      0.02     2.166055e+06   \n",
              "77                   191                      0.00     8.472019e+03   \n",
              "78                   451                      0.00     1.020595e+04   \n",
              "79                   237                      0.02     9.650998e+02   \n",
              "\n",
              "    Inspiratory Indicies (Aeration Data)  Subject Number  ...  \\\n",
              "0                                  203.0               1  ...   \n",
              "1                                  104.0               2  ...   \n",
              "2                                   98.0               3  ...   \n",
              "3                                   31.0               4  ...   \n",
              "4                                  152.0               5  ...   \n",
              "..                                   ...             ...  ...   \n",
              "75                                 262.0              76  ...   \n",
              "76                                 124.0              77  ...   \n",
              "77                                 124.0              78  ...   \n",
              "78                                  95.0              79  ...   \n",
              "79                                 118.0              80  ...   \n",
              "\n",
              "       Smoking Frequency  Time as a smoker   History of Vaping (Y/N)  \\\n",
              "0                    NaN                NaN                        N   \n",
              "1                    NaN                NaN                        Y   \n",
              "2                    NaN                NaN                        N   \n",
              "3                    NaN                NaN                        N   \n",
              "4       3-4 times yearly           10 years                        N   \n",
              "..                   ...                ...                      ...   \n",
              "75       3-4 times daily           3 years                         N   \n",
              "76  less than once daily            7 years                        N   \n",
              "77              10 daily            3 years                        Y   \n",
              "78                   NaN                NaN                        Y   \n",
              "79                   NaN                NaN                        N   \n",
              "\n",
              "    Current Vaper (Y/N) How long since you quit vaping Vaping Frequency  \\\n",
              "0                   NaN                            NaN              NaN   \n",
              "1                     Y                            NaN    4 times daily   \n",
              "2                   NaN                            NaN              NaN   \n",
              "3                   NaN                            NaN              NaN   \n",
              "4                   NaN                            NaN              NaN   \n",
              "..                  ...                            ...              ...   \n",
              "75                  NaN                            NaN              NaN   \n",
              "76                  NaN                            NaN              NaN   \n",
              "77                    Y                            NaN     once hourly    \n",
              "78                    Y                            NaN   10 times daily   \n",
              "79                  NaN                            NaN              NaN   \n",
              "\n",
              "   Time as a vaper Trial Classification  Chest Depth [mm] Chest Width [mm]  \n",
              "0              NaN         Normal Female             20.0             28.0  \n",
              "1          2 years      Asthmatic Female             25.0             33.0  \n",
              "2              NaN         Normal Female             22.0             30.0  \n",
              "3              NaN           Normal Male             27.0             38.0  \n",
              "4              NaN           Normal Male             23.0             30.0  \n",
              "..             ...                   ...              ...              ...  \n",
              "75             NaN         Smoker Female             21.0             30.0  \n",
              "76             NaN         Smoker Female             20.0             25.0  \n",
              "77         4 years            Vaper Male             25.0             34.0  \n",
              "78        3 years             Vaper Male             25.5             36.0  \n",
              "79             NaN      Asthmatic Female             23.0             27.0  \n",
              "\n",
              "[80 rows x 30 columns]"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "81f9600c-acd1-4067-ba4f-0e72c1aa0934",
      "metadata": {
        "id": "81f9600c-acd1-4067-ba4f-0e72c1aa0934"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
        "\n",
        "cc = ['Sex (M/F)', 'Asthma (Y/N)',\t'Medication', 'Dosage Frequency',\t'History of Smoking (Y/N)',\t'Current Smoker (Y/N)',\t'How long since you quit smoking',\t'Smoking Frequency',\t'Time as a smoker ', \t'History of Vaping (Y/N)',\t'Current Vaper (Y/N)',\n",
        "      'Time as a vaper'\t,'Trial Classification ',\n",
        "'How long since you quit vaping', 'Vaping Frequency']\n",
        "\n",
        "le = {}\n",
        "for column in cc:\n",
        "    le[column] = LabelEncoder()\n",
        "    df1[column] = le[column].fit_transform(df1[column].astype(str))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3e2d8600-cde7-4cb8-b69d-3f98151b171c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "3e2d8600-cde7-4cb8-b69d-3f98151b171c",
        "outputId": "6601168f-c852-4e30-c11e-66621ca82b28"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 80,\n  \"fields\": [\n    {\n      \"column\": \"Pressure [cmH2O]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.891339074425897,\n        \"min\": -3.63678874271341,\n        \"max\": 6.87664184094188,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          1.59847056833126,\n          -2.13487008790551,\n          -0.439847606050884\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Flow [L/s]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.39398168830881125,\n        \"min\": -0.6869290997877,\n        \"max\": 0.803866861078949,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          0.668904555847893,\n          -0.416795693205744,\n          0.687225860268968\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"V_tidal [L]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3435454590905637,\n        \"min\": -0.516724578422439,\n        \"max\": 1.21424460391475,\n        \"num_unique_values\": 67,\n        \"samples\": [\n          0.152191403748026,\n          -0.324034912955189,\n          -0.117277625093464\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Chest [mm]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 160.2114803168255,\n        \"min\": 108.0,\n        \"max\": 1234.75674890259,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          907.965228454407,\n          906.331922410506,\n          945.852022846883\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Abd [mm]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 148.8151677362107,\n        \"min\": 108.0,\n        \"max\": 1221.19635873768,\n        \"num_unique_values\": 80,\n        \"samples\": [\n          725.643558415062,\n          843.390389712376,\n          920.476299061363\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Inspiratory Indicies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 234,\n        \"min\": 3,\n        \"max\": 1391,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          110,\n          451,\n          49\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time (Aeration Data)_[s]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.007224323298936447,\n        \"min\": 0.0,\n        \"max\": 0.02,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          0.02\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Global Aeration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 947299.1369514441,\n        \"min\": 369.013368864681,\n        \"max\": 3224063.50989841,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          2567.69012512343,\n          2536.681081177\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Inspiratory Indicies (Aeration Data)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 125.2753423160562,\n        \"min\": 1.0,\n        \"max\": 508.0,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          139.0,\n          39.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df1"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-66c97dd5-1402-4444-ac49-73ddaf45bb85\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pressure [cmH2O]</th>\n",
              "      <th>Flow [L/s]</th>\n",
              "      <th>V_tidal [L]</th>\n",
              "      <th>Chest [mm]</th>\n",
              "      <th>Abd [mm]</th>\n",
              "      <th>Inspiratory Indicies</th>\n",
              "      <th>Time (Aeration Data)_[s]</th>\n",
              "      <th>Global Aeration</th>\n",
              "      <th>Inspiratory Indicies (Aeration Data)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-2.134870</td>\n",
              "      <td>0.579332</td>\n",
              "      <td>0.169331</td>\n",
              "      <td>906.331922</td>\n",
              "      <td>843.390390</td>\n",
              "      <td>328</td>\n",
              "      <td>0.02</td>\n",
              "      <td>2.536681e+03</td>\n",
              "      <td>164.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.823758</td>\n",
              "      <td>-0.510568</td>\n",
              "      <td>-0.248067</td>\n",
              "      <td>1034.860261</td>\n",
              "      <td>908.856548</td>\n",
              "      <td>63</td>\n",
              "      <td>0.02</td>\n",
              "      <td>5.109781e+03</td>\n",
              "      <td>31.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.362455</td>\n",
              "      <td>0.498663</td>\n",
              "      <td>0.375113</td>\n",
              "      <td>996.441635</td>\n",
              "      <td>861.775149</td>\n",
              "      <td>315</td>\n",
              "      <td>0.02</td>\n",
              "      <td>5.250633e+03</td>\n",
              "      <td>157.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.686591</td>\n",
              "      <td>-0.314929</td>\n",
              "      <td>-0.039403</td>\n",
              "      <td>975.759126</td>\n",
              "      <td>863.861363</td>\n",
              "      <td>96</td>\n",
              "      <td>0.02</td>\n",
              "      <td>1.026124e+04</td>\n",
              "      <td>48.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.354024</td>\n",
              "      <td>-0.156978</td>\n",
              "      <td>-0.117278</td>\n",
              "      <td>780.282420</td>\n",
              "      <td>676.369948</td>\n",
              "      <td>110</td>\n",
              "      <td>0.02</td>\n",
              "      <td>7.397614e+03</td>\n",
              "      <td>55.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>2.370886</td>\n",
              "      <td>0.193579</td>\n",
              "      <td>-0.262548</td>\n",
              "      <td>949.930264</td>\n",
              "      <td>891.145129</td>\n",
              "      <td>57</td>\n",
              "      <td>0.02</td>\n",
              "      <td>5.005083e+03</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>0.933335</td>\n",
              "      <td>-0.352174</td>\n",
              "      <td>-0.094003</td>\n",
              "      <td>964.527779</td>\n",
              "      <td>884.984279</td>\n",
              "      <td>387</td>\n",
              "      <td>0.02</td>\n",
              "      <td>1.025522e+04</td>\n",
              "      <td>193.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>0.300384</td>\n",
              "      <td>-0.156978</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1234.756749</td>\n",
              "      <td>1221.196359</td>\n",
              "      <td>145</td>\n",
              "      <td>0.00</td>\n",
              "      <td>6.703179e+03</td>\n",
              "      <td>206.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>-0.439848</td>\n",
              "      <td>0.273390</td>\n",
              "      <td>0.273757</td>\n",
              "      <td>841.434564</td>\n",
              "      <td>792.669315</td>\n",
              "      <td>279</td>\n",
              "      <td>0.02</td>\n",
              "      <td>1.732984e+04</td>\n",
              "      <td>139.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>-2.810733</td>\n",
              "      <td>0.668905</td>\n",
              "      <td>0.748768</td>\n",
              "      <td>849.094881</td>\n",
              "      <td>781.440806</td>\n",
              "      <td>269</td>\n",
              "      <td>0.02</td>\n",
              "      <td>2.274803e+06</td>\n",
              "      <td>134.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>80 rows × 9 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-66c97dd5-1402-4444-ac49-73ddaf45bb85')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-66c97dd5-1402-4444-ac49-73ddaf45bb85 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-66c97dd5-1402-4444-ac49-73ddaf45bb85');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ba878afb-db96-4b01-bb0d-6762f6ae2287\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ba878afb-db96-4b01-bb0d-6762f6ae2287')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ba878afb-db96-4b01-bb0d-6762f6ae2287 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_5c970b39-50fb-4877-af9a-f061f0e480da\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df1')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_5c970b39-50fb-4877-af9a-f061f0e480da button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df1');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "    Pressure [cmH2O]  Flow [L/s]  V_tidal [L]   Chest [mm]     Abd [mm]  \\\n",
              "0          -2.134870    0.579332     0.169331   906.331922   843.390390   \n",
              "1           1.823758   -0.510568    -0.248067  1034.860261   908.856548   \n",
              "2          -1.362455    0.498663     0.375113   996.441635   861.775149   \n",
              "3           0.686591   -0.314929    -0.039403   975.759126   863.861363   \n",
              "4           0.354024   -0.156978    -0.117278   780.282420   676.369948   \n",
              "..               ...         ...          ...          ...          ...   \n",
              "75          2.370886    0.193579    -0.262548   949.930264   891.145129   \n",
              "76          0.933335   -0.352174    -0.094003   964.527779   884.984279   \n",
              "77          0.300384   -0.156978     0.000000  1234.756749  1221.196359   \n",
              "78         -0.439848    0.273390     0.273757   841.434564   792.669315   \n",
              "79         -2.810733    0.668905     0.748768   849.094881   781.440806   \n",
              "\n",
              "    Inspiratory Indicies  Time (Aeration Data)_[s]  Global Aeration  \\\n",
              "0                    328                      0.02     2.536681e+03   \n",
              "1                     63                      0.02     5.109781e+03   \n",
              "2                    315                      0.02     5.250633e+03   \n",
              "3                     96                      0.02     1.026124e+04   \n",
              "4                    110                      0.02     7.397614e+03   \n",
              "..                   ...                       ...              ...   \n",
              "75                    57                      0.02     5.005083e+03   \n",
              "76                   387                      0.02     1.025522e+04   \n",
              "77                   145                      0.00     6.703179e+03   \n",
              "78                   279                      0.02     1.732984e+04   \n",
              "79                   269                      0.02     2.274803e+06   \n",
              "\n",
              "    Inspiratory Indicies (Aeration Data)  \n",
              "0                                  164.0  \n",
              "1                                   31.0  \n",
              "2                                  157.0  \n",
              "3                                   48.0  \n",
              "4                                   55.0  \n",
              "..                                   ...  \n",
              "75                                  28.0  \n",
              "76                                 193.0  \n",
              "77                                 206.0  \n",
              "78                                 139.0  \n",
              "79                                 134.0  \n",
              "\n",
              "[80 rows x 9 columns]"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "726e4fda-592f-48d9-8714-dbfb2d5fbafc",
      "metadata": {
        "id": "726e4fda-592f-48d9-8714-dbfb2d5fbafc"
      },
      "outputs": [],
      "source": [
        "df1= df1.drop('Global Aeration', axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "b9cf69d7-5ec2-4822-ac02-c8c1a4edb948",
      "metadata": {
        "id": "b9cf69d7-5ec2-4822-ac02-c8c1a4edb948"
      },
      "outputs": [],
      "source": [
        "output_file_path = \"final_data.csv\"\n",
        "df1.to_csv(output_file_path, index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "KMnQa4A8op9F",
      "metadata": {
        "id": "KMnQa4A8op9F"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_squared_error"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "7zFzOTtSotoz",
      "metadata": {
        "id": "7zFzOTtSotoz"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(\"final_data.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "KEAtZQOnoxmR",
      "metadata": {
        "id": "KEAtZQOnoxmR"
      },
      "outputs": [],
      "source": [
        "cleaned_df = df.replace([np.inf, -np.inf], np.nan).dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "2moebxvMo117",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "2moebxvMo117",
        "outputId": "34869e59-3681-4357-9e95-ea52eea25d3c"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "cleaned_df"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-91e8194f-f1d7-4b96-af99-071cfdb8d623\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pressure [cmH2O]</th>\n",
              "      <th>Flow [L/s]</th>\n",
              "      <th>V_tidal [L]</th>\n",
              "      <th>Chest [mm]</th>\n",
              "      <th>Abd [mm]</th>\n",
              "      <th>Inspiratory Indicies</th>\n",
              "      <th>Time (Aeration Data)_[s]</th>\n",
              "      <th>Inspiratory Indicies (Aeration Data)</th>\n",
              "      <th>Subject Number</th>\n",
              "      <th>Sex (M/F)</th>\n",
              "      <th>...</th>\n",
              "      <th>Smoking Frequency</th>\n",
              "      <th>Time as a smoker</th>\n",
              "      <th>History of Vaping (Y/N)</th>\n",
              "      <th>Current Vaper (Y/N)</th>\n",
              "      <th>How long since you quit vaping</th>\n",
              "      <th>Vaping Frequency</th>\n",
              "      <th>Time as a vaper</th>\n",
              "      <th>Trial Classification</th>\n",
              "      <th>Chest Depth [mm]</th>\n",
              "      <th>Chest Width [mm]</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-3.400773</td>\n",
              "      <td>0.713827</td>\n",
              "      <td>0.299421</td>\n",
              "      <td>830.612330</td>\n",
              "      <td>735.557370</td>\n",
              "      <td>407</td>\n",
              "      <td>0.02</td>\n",
              "      <td>203.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>16</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>21</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>20.0</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.703454</td>\n",
              "      <td>-0.610317</td>\n",
              "      <td>0.117958</td>\n",
              "      <td>1048.261347</td>\n",
              "      <td>1031.678708</td>\n",
              "      <td>209</td>\n",
              "      <td>0.02</td>\n",
              "      <td>104.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>16</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>33.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.619927</td>\n",
              "      <td>0.459778</td>\n",
              "      <td>0.660224</td>\n",
              "      <td>825.950946</td>\n",
              "      <td>713.628807</td>\n",
              "      <td>196</td>\n",
              "      <td>0.02</td>\n",
              "      <td>98.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>16</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>21</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>22.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.823758</td>\n",
              "      <td>-0.510568</td>\n",
              "      <td>-0.248067</td>\n",
              "      <td>1034.860261</td>\n",
              "      <td>908.856548</td>\n",
              "      <td>63</td>\n",
              "      <td>0.02</td>\n",
              "      <td>31.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>16</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>21</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>27.0</td>\n",
              "      <td>38.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.386208</td>\n",
              "      <td>0.193579</td>\n",
              "      <td>0.003872</td>\n",
              "      <td>889.221901</td>\n",
              "      <td>754.629966</td>\n",
              "      <td>304</td>\n",
              "      <td>0.02</td>\n",
              "      <td>152.0</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>21</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>23.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>-1.287359</td>\n",
              "      <td>0.386368</td>\n",
              "      <td>0.671871</td>\n",
              "      <td>788.268708</td>\n",
              "      <td>691.503282</td>\n",
              "      <td>524</td>\n",
              "      <td>0.02</td>\n",
              "      <td>262.0</td>\n",
              "      <td>76</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>21</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>21.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>-1.823758</td>\n",
              "      <td>0.510967</td>\n",
              "      <td>0.297882</td>\n",
              "      <td>740.842547</td>\n",
              "      <td>601.545280</td>\n",
              "      <td>248</td>\n",
              "      <td>0.02</td>\n",
              "      <td>124.0</td>\n",
              "      <td>77</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>13</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>21</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>20.0</td>\n",
              "      <td>25.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>4.087364</td>\n",
              "      <td>-0.416796</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>974.658648</td>\n",
              "      <td>912.772954</td>\n",
              "      <td>191</td>\n",
              "      <td>0.00</td>\n",
              "      <td>124.0</td>\n",
              "      <td>78</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>24</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>25.0</td>\n",
              "      <td>34.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>-1.898854</td>\n",
              "      <td>0.522983</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>958.539885</td>\n",
              "      <td>843.194807</td>\n",
              "      <td>451</td>\n",
              "      <td>0.00</td>\n",
              "      <td>95.0</td>\n",
              "      <td>79</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>16</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>25.5</td>\n",
              "      <td>36.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>-1.083527</td>\n",
              "      <td>0.386368</td>\n",
              "      <td>0.128367</td>\n",
              "      <td>797.721864</td>\n",
              "      <td>701.581229</td>\n",
              "      <td>237</td>\n",
              "      <td>0.02</td>\n",
              "      <td>118.0</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>16</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>21</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>27.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>79 rows × 29 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-91e8194f-f1d7-4b96-af99-071cfdb8d623')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-91e8194f-f1d7-4b96-af99-071cfdb8d623 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-91e8194f-f1d7-4b96-af99-071cfdb8d623');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1e9e6dc2-5609-410f-a461-92d54554504c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1e9e6dc2-5609-410f-a461-92d54554504c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1e9e6dc2-5609-410f-a461-92d54554504c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_b67fb6f3-2405-4c71-8e08-757d1c6433d5\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('cleaned_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_b67fb6f3-2405-4c71-8e08-757d1c6433d5 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('cleaned_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "    Pressure [cmH2O]  Flow [L/s]  V_tidal [L]   Chest [mm]     Abd [mm]  \\\n",
              "0          -3.400773    0.713827     0.299421   830.612330   735.557370   \n",
              "1           2.703454   -0.610317     0.117958  1048.261347  1031.678708   \n",
              "2          -1.619927    0.459778     0.660224   825.950946   713.628807   \n",
              "3           1.823758   -0.510568    -0.248067  1034.860261   908.856548   \n",
              "4          -0.386208    0.193579     0.003872   889.221901   754.629966   \n",
              "..               ...         ...          ...          ...          ...   \n",
              "75         -1.287359    0.386368     0.671871   788.268708   691.503282   \n",
              "76         -1.823758    0.510967     0.297882   740.842547   601.545280   \n",
              "77          4.087364   -0.416796     0.000000   974.658648   912.772954   \n",
              "78         -1.898854    0.522983     0.000000   958.539885   843.194807   \n",
              "79         -1.083527    0.386368     0.128367   797.721864   701.581229   \n",
              "\n",
              "    Inspiratory Indicies  Time (Aeration Data)_[s]  \\\n",
              "0                    407                      0.02   \n",
              "1                    209                      0.02   \n",
              "2                    196                      0.02   \n",
              "3                     63                      0.02   \n",
              "4                    304                      0.02   \n",
              "..                   ...                       ...   \n",
              "75                   524                      0.02   \n",
              "76                   248                      0.02   \n",
              "77                   191                      0.00   \n",
              "78                   451                      0.00   \n",
              "79                   237                      0.02   \n",
              "\n",
              "    Inspiratory Indicies (Aeration Data)  Subject Number  Sex (M/F)  ...  \\\n",
              "0                                  203.0               1          0  ...   \n",
              "1                                  104.0               2          0  ...   \n",
              "2                                   98.0               3          0  ...   \n",
              "3                                   31.0               4          1  ...   \n",
              "4                                  152.0               5          1  ...   \n",
              "..                                   ...             ...        ...  ...   \n",
              "75                                 262.0              76          0  ...   \n",
              "76                                 124.0              77          0  ...   \n",
              "77                                 124.0              78          1  ...   \n",
              "78                                  95.0              79          1  ...   \n",
              "79                                 118.0              80          0  ...   \n",
              "\n",
              "    Smoking Frequency  Time as a smoker   History of Vaping (Y/N)  \\\n",
              "0                  16                 13                        0   \n",
              "1                  16                 13                        1   \n",
              "2                  16                 13                        0   \n",
              "3                  16                 13                        0   \n",
              "4                   9                  1                        0   \n",
              "..                ...                ...                      ...   \n",
              "75                  8                  7                        0   \n",
              "76                 13                 11                        0   \n",
              "77                  1                  6                        1   \n",
              "78                 16                 13                        1   \n",
              "79                 16                 13                        0   \n",
              "\n",
              "    Current Vaper (Y/N)  How long since you quit vaping  Vaping Frequency  \\\n",
              "0                     2                               5                21   \n",
              "1                     1                               5                 8   \n",
              "2                     2                               5                21   \n",
              "3                     2                               5                21   \n",
              "4                     2                               5                21   \n",
              "..                  ...                             ...               ...   \n",
              "75                    2                               5                21   \n",
              "76                    2                               5                21   \n",
              "77                    1                               5                24   \n",
              "78                    1                               5                 1   \n",
              "79                    2                               5                21   \n",
              "\n",
              "    Time as a vaper  Trial Classification   Chest Depth [mm]  Chest Width [mm]  \n",
              "0                12                      2              20.0              28.0  \n",
              "1                 3                      0              25.0              33.0  \n",
              "2                12                      2              22.0              30.0  \n",
              "3                12                      3              27.0              38.0  \n",
              "4                12                      3              23.0              30.0  \n",
              "..              ...                    ...               ...               ...  \n",
              "75               12                      4              21.0              30.0  \n",
              "76               12                      4              20.0              25.0  \n",
              "77                9                      7              25.0              34.0  \n",
              "78                6                      7              25.5              36.0  \n",
              "79               12                      0              23.0              27.0  \n",
              "\n",
              "[79 rows x 29 columns]"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cleaned_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "TMSSmbMff5W8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TMSSmbMff5W8",
        "outputId": "a3c7d96d-2a68-43b5-c819-a2763d865c39"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index(['Pressure [cmH2O]', 'Flow [L/s]', 'V_tidal [L]', 'Chest [mm]',\n",
            "       'Abd [mm]', 'Inspiratory Indicies', 'Time (Aeration Data)_[s]',\n",
            "       'Inspiratory Indicies (Aeration Data)', 'Subject Number', 'Sex (M/F)',\n",
            "       'Height [cm]', 'Weight [kg]', 'Age [years]', 'Asthma (Y/N)',\n",
            "       'Medication', 'Dosage Frequency', 'History of Smoking (Y/N)',\n",
            "       'Current Smoker (Y/N)', 'How long since you quit smoking',\n",
            "       'Smoking Frequency', 'Time as a smoker ', 'History of Vaping (Y/N)',\n",
            "       'Current Vaper (Y/N)', 'How long since you quit vaping',\n",
            "       'Vaping Frequency', 'Time as a vaper', 'Trial Classification ',\n",
            "       'Chest Depth [mm]', 'Chest Width [mm]'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "print(cleaned_df.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "fAAb-K9Td8Y2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "fAAb-K9Td8Y2",
        "outputId": "1ca96fee-bf8f-4e00-999a-30117ce62a65"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "\n",
        "# Example column names for the plots\n",
        "scatter_x = 'Age [years]'\n",
        "scatter_y = 'V_tidal [L]'\n",
        "scatter_z='Height [cm]'\n",
        "hist_column1 = 'Sex'\n",
        "hist_column2 = 'V_tidal'\n",
        "cleaned_again_df = df.dropna(subset=[scatter_x, scatter_y])\n",
        "# Plotting the scatter plot\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.scatter(cleaned_again_df[scatter_x], df[scatter_y], alpha=0.5)\n",
        "plt.title('Dot Frequency Graph (Scatter Plot)')\n",
        "plt.xlabel(scatter_x)\n",
        "plt.ylabel(scatter_y)\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "# line plot\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.scatter(cleaned_again_df[scatter_z], df[scatter_y], alpha=0.5)\n",
        "plt.title('Dot Frequency Graph (Scatter Plot)')\n",
        "plt.xlabel(scatter_z)\n",
        "plt.ylabel(scatter_y)\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "# Define the columns\n",
        "gender_column = 'Sex (M/F)'\n",
        "tidal_volume_column = 'V_tidal [L]'\n",
        "\n",
        "# Define the columns\n",
        "time_as_smoker_column = 'Time as a smoker '\n",
        "\n",
        "# Drop rows with missing values in the relevant columns\n",
        "cleaned_4_df = df.dropna(subset=[tidal_volume_column, time_as_smoker_column])\n",
        "\n",
        "\n",
        "# Plotting the line plot (if applicable)\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.lineplot(x=cleaned_4_df[time_as_smoker_column], y=cleaned_4_df[tidal_volume_column], marker='o')\n",
        "plt.title('Line Plot of Lung Tidal Volume vs Time as Smoker')\n",
        "plt.xlabel('Time as Smoker')\n",
        "plt.ylabel('Lung Tidal Volume [L]')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "nLcdqO6xo7UR",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nLcdqO6xo7UR",
        "outputId": "df9ff34c-dbb4-4ffe-c941-af6aa510b7ba"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      Actual  Predicted\n",
            "30  0.591854   0.410858\n",
            "0   0.299421   0.528024\n",
            "22  0.255701   0.410912\n",
            "31 -0.071450   0.309739\n",
            "18  0.000000   0.284764\n",
            "28  0.273757   0.458182\n",
            "10  1.214245   0.491757\n",
            "71  0.000000  -0.030640\n",
            "4   0.003872   0.432612\n",
            "12  0.000000   0.353662\n",
            "49  1.187187   0.218447\n",
            "33  0.000000   0.217752\n",
            "67 -0.184823  -0.137784\n",
            "35 -0.260213  -0.112566\n",
            "69  0.044608   0.296136\n",
            "45  0.410221   0.321980\n",
            "Mean Squared Error: 0.14264238198237172\n"
          ]
        }
      ],
      "source": [
        "X = cleaned_df.drop(columns=['V_tidal [L]'])\n",
        "y = cleaned_df['V_tidal [L]']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "regressor = RandomForestRegressor(n_estimators=100, random_state=42)\n",
        "\n",
        "regressor.fit(X_train, y_train)\n",
        "\n",
        "y_pred = regressor.predict(X_test)\n",
        "results = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
        "print(results)\n",
        "# Evaluate the model using Mean Squared Error (MSE)\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "print(\"Mean Squared Error:\", mse)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}